Improved Fuzzy C-Means Based on the Optimal Number of Clusters

Abstract:

According to the number of cluster centers, initial cluster centers, fuzzy factor, iterations and threshold, Fuzzy C-means clustering algorithm (FCM) clusters the data set. FCM will encounter the initialization problem of clustering prototype. Firstly, the article combines the maximum and minimum distance algorithm and K-means algorithm to determine the number of clusters and the initial cluster centers. Secondly, the article determines the optimal number of clusters with Silhouette indicators. Finally, the article improves the convergence rate of FCM by revising membership constantly. The improved FCM has good clustering effect, enhances the optimized capability, and improves the efficiency and effectiveness of the clustering. It has better tightness in the class, scatter among classes and cluster stability and faster convergence rate than the traditional FCM clustering method.

• Periodical: Applied Mechanics and Materials (Volume 392)
• Pages: 803-807
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.392.803
• Online since: September 2013
• Authors: Xue Bo Feng, Fang Yao, Zhi Gang Li, Xiao Jing Yang
• Keywords: Convergence Rate, Fuzzy C-Means, K-Mean, Maximum Distance, Minimum Distance

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